A fault localization approach based on multi-system PCA and dynamic SDG: Application in train lifting equipment

Abstract

The development of the current rail transportation is increasingly rapid, and train maintenance is closely related to the high-efficiency running of rail transportation. Train lifting equipment (TLE) is important for train maintenance, for which stable operation is crucial. TLE fault is characterized by early abnormally weak and capable of propagation. Early anomaly detection and fault diagnosis are the keys to TLE fault maintenance, and this paper proposes a TLE fault localization methodology to address these two problems. For early anomaly detection, a combination of sequence-based and principal component analysis-based methods is utilized to address the problem. For fault diagnosis, the signed directed graph-based method is utilized to ensure the completeness of reasoning results, and the system division mechanism and dynamic division mechanism are utilized to narrow down the scope of faults and accelerate the reasoning process, respectively. The experiments prove that the proposed method can reduce the loss caused by equipment faults and improve the reliability and stability of the equipment.